Canada Thistle (<i>Cirsium arvense</i>) Suppression by Sudangrass Interference and Defoliation
Bibliographic record
Abstract
Canada thistle is difficult to manage in organic farming systems and others with reduced reliance on herbicides. Previous field studies found that defoliation or sudangrass interference suppressed Canada thistle. Our objective was to understand the factors causing suppression of Canada thistle observed in the field. Three greenhouse studies were conducted utilizing frequency of defoliation, sudangrass interference and defoliation, and interspecific phytotoxicity to discern mechanisms of Canada thistle suppression. Increased defoliation frequency (up to four defoliations) decreased Canada thistle shoot height, shoot and root mass, and root-to-shoot ratio. Plants with larger root mass had greater shoot mass and number (r= 0.87 and 0.73, respectively), indicating a probable interdependence of root size (carbohydrate reserves), bud density, and subsequent shoot growth. In the sudangrass interference and defoliation study, Canada thistle shoot dry mass was 38.7, 2.76, and 0.39 g pot−1in the defoliation only, sudangrass interference only, and defoliation + interference + surface mulch treatments, respectively. Sudangrass interference by itself was effective in suppressing thistle growth; combining interference with defoliation did not further reduce growth (2.76 and 2.83 g pot−1, respectively). In the experiment minimizing interspecific competition, we found no evidence of sudangrass having a phytotoxic effect on Canada thistle. Overall results indicate that sudangrass competition or frequent shoot removal suppresses growth of Canada thistle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".